A surface diaphragm electromyogram-based cross-body position respiratory monitoring method

By combining surface diaphragm electromyography signals and inertial motion data, personalized, online, real-time, multi-posture robust respiratory monitoring was achieved, solving the problem of signal drift under changes in body position and motion, and providing high-precision respiratory parameter estimation.

CN122296920APending Publication Date: 2026-06-30INST OF HEALTH & MEDICINE HEFEI COMPREHENSIVE NAT SCI CENT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-06-01
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing respiratory monitoring protocols suffer from severe baseline drift due to changes in body position and during movement, making it impossible to achieve personalized, online, real-time, and multi-posture robust monitoring of diaphragmatic electromyography signals.

Method used

By synchronously acquiring surface diaphragm electromyography signals and triaxial inertial motion data, real-time attitude estimation and classification are performed, an individualized attitude-baseline mapping dictionary is constructed, and a two-level cascaded baseline correction method is adopted, combined with dynamic weighted Mahony complementary filtering and incremental recursive least squares algorithm to achieve signal correction and amplitude normalization.

Benefits of technology

It reduces respiratory rate estimation error under multiple postures, is suitable for real-time operation of embedded microprocessors, outputs multi-dimensional clinical monitoring indicators, and improves the accuracy and stability of respiratory monitoring.

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Abstract

This invention discloses a cross-postural posture respiratory monitoring method based on surface diaphragm electromyography (EMG) signals, belonging to the field of respiratory monitoring technology. Specifically, it includes: simultaneously acquiring surface diaphragm EMG signals and inertial motion data; real-time estimation of trunk posture and identification of switching events; retrieving baseline parameters of the target posture from a personalized posture-baseline mapping dictionary (PBD dictionary); performing a two-level cascaded baseline correction consisting of an exponential transition model and an incremental recursive least squares algorithm; performing amplitude alignment using a normalized gain factor based on the target posture or current posture; and inputting respiratory parameters into a monitoring network. This invention aims to solve the signal drift problem caused by postural changes and improve monitoring accuracy under free movement conditions.
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